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Abstract The emergence of Shanghai as an international city necessitate that the industrial sector should be less polluting as much as possible because pollution will alienate businesses, professionals and the relocation or setting up of corporate offices that enhances green economic growth. To ensure that policy targeting industrial emissions are achieved, standard mean based models underperforms, so exploring a quantile framework, varying effects of economic development, energy structure, energy efficiency, industrial structure and urbanization across the levels of carbon dioxide emissions were analyzed for the industrial sector of Shanghai where estimates both described the distribution of carbon dioxide emissions and their marginal effects on different quantiles. Explanatory variables were emission friendly and did not exert same effects on the acceleration of carbon emissions. Averagely, the coefficient of GDP, ENS, ENE, IND, and URB at Q(0.50) = 0.06306, 0.39285, 0.07375, 0.52193 and 0.48357 respectively, implying that a unit increase in these economic indicators will increase carbon dioxide emissions by 6.3, 39.3, 7.3, 52.2 and 48.4 percent approximately and the model goodness of fit was 87.8 percent. Urbanization had the greatest impact on carbon dioxide emissions across all quantiles, showing that it is a major driving force that increase carbon emissions, followed by energy structure, industrial structure, economic growth and energy efficiency. An optimized energy efficiency will be the best mitigating variable that could curb carbon dioxide emissions, then followed by energy structure. Favorable economic policies from the government have contributed enormously to the development of the industrial sector, however, additional policies are needed to steer the city from an investment and export growth model to consumption (services). [ABSTRACT FROM AUTHOR]

This research examines the impact of GDP per capita (gross domestic product), energy intensity (EI), carbon intensity (CI), and total population on carbon dioxide emissions in China's transport industry using quantile analysis from 1980 to 2010. Obviously the study on carbon dioxide has garnered attention globally due to climate change and its relation to green house gas emissions and several other factors, and considering the alarming pace of industrialization and urbanization in China which has led to rapid economic growth and high energy consumption. Also, the percentage of oil consumption to total oil demand in the transport industry of China was 38.2% for the year 2010 which has significantly raised the emission level of carbon dioxide. In this study, having confirmed stationarity and that there exist a long term relationship among our variables (carbon emission, gross domestic product, energy intensity, carbon intensity, and urbanization), we checked which variable(s) has a greater impact on carbon emission on different quantiles. Our quantile estimates showed how the effects of the independent variables (gross domestic product, energy intensity, carbon intensity, and urbanization) varies across the levels of the dependent variable (carbon emission), the results showed that these effect are not constant across the spectrum of the dependent variable. Unlike the gross domestic product, energy intensity, carbon intensity, and urbanization had an inconsistent effects across the spectrum of carbon emissions. All variables were statistically significant in all the spectrum of carbon emissions except for urbanization, which was only significant at the tail ends of the distribution (urbanization was only significant at 10th percentile and 90th percentile respectively). The results therefore shows clearly that GDP, energy intensity, carbon intensity has a greater impact on carbon emission than urbanization, this makes sense to an extent in real life comparing the fact that China is still in the process of urbanization, so not all cities are urban for now. However, this shows that where urbanization exist, it can influence carbon emissions alongside other factors immensely. [ABSTRACT FROM AUTHOR]